Replace all elements of Python NumPy Array that are greater than some value

Question

I have a 2D NumPy array and would like to replace all values in it greater than or equal to a threshold T with 255.0. To my knowledge, the most fundamental way would be:

shape = arr.shape result = np.zeros(shape) for x in range(0, shape[0]):     for y in range(0, shape[1]):         if arr[x, y] >= T:             result[x, y] = 255 

1. What is the most concise and pythonic way to do this?

2. Is there a faster (possibly less concise and/or less pythonic) way to do this?

This will be part of a window/level adjustment subroutine for MRI scans of the human head. The 2D numpy array is the image pixel data.

Answer 1

I think both the fastest and most concise way to do this is to use NumPy's built-in Fancy indexing. If you have an ndarray named arr, you can replace all elements >255 with a value x as follows:

arr[arr > 255] = x 

I ran this on my machine with a 500 x 500 random matrix, replacing all values >0.5 with 5, and it took an average of 7.59ms.

In [1]: import numpy as np In [2]: A = np.random.rand(500, 500) In [3]: timeit A[A > 0.5] = 5 100 loops, best of 3: 7.59 ms per loop 

Answer 2

Since you actually want a different array which is arr where arr < 255, and 255 otherwise, this can be done simply:

result = np.minimum(arr, 255) 

More generally, for a lower and/or upper bound:

result = np.clip(arr, 0, 255) 

If you just want to access the values over 255, or something more complicated, @mtitan8's answer is more general, but np.clip and np.minimum (or np.maximum) are nicer and much faster for your case:

In [292]: timeit np.minimum(a, 255) 100000 loops, best of 3: 19.6 µs per loop  In [293]: %%timeit    .....: c = np.copy(a)    .....: c[a>255] = 255    .....:  10000 loops, best of 3: 86.6 µs per loop 

If you want to do it in-place (i.e., modify arr instead of creating result) you can use the out parameter of np.minimum:

np.minimum(arr, 255, out=arr) 

or

np.clip(arr, 0, 255, arr) 

(the out= name is optional since the arguments in the same order as the function's definition.)

For in-place modification, the boolean indexing speeds up a lot (without having to make and then modify the copy separately), but is still not as fast as minimum:

In [328]: %%timeit    .....: a = np.random.randint(0, 300, (100,100))    .....: np.minimum(a, 255, a)    .....:  100000 loops, best of 3: 303 µs per loop  In [329]: %%timeit    .....: a = np.random.randint(0, 300, (100,100))    .....: a[a>255] = 255    .....:  100000 loops, best of 3: 356 µs per loop 

For comparison, if you wanted to restrict your values with a minimum as well as a maximum, without clip you would have to do this twice, with something like

np.minimum(a, 255, a) np.maximum(a, 0, a) 

or,

a[a>255] = 255 a[a<0] = 0